An empirical study into class testability
Journal of Systems and Software - Special issue: Selected papers from the 4th source code analysis and manipulation (SCAM 2004) workshop
Data Mining Static Code Attributes to Learn Defect Predictors
IEEE Transactions on Software Engineering
Adequate and Precise Evaluation of Quality Models in Software Engineering Studies
PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering
Implications of ceiling effects in defect predictors
Proceedings of the 4th international workshop on Predictor models in software engineering
Techniques for evaluating fault prediction models
Empirical Software Engineering
Identifying poorly documented object oriented software components
International Journal of Hybrid Intelligent Systems
Exhaustive and heuristic search approaches for learning a software defect prediction model
Engineering Applications of Artificial Intelligence
Probabilistic and analytical estimation of software development team size
International Journal of Hybrid Intelligent Systems
How good is your blind spot sampling policy
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Defect prediction using social network analysis on issue repositories
Proceedings of the 2011 International Conference on Software and Systems Process
A framework for defect prediction in specific software project contexts
CEE-SET'08 Proceedings of the Third IFIP TC 2 Central and East European conference on Software engineering techniques
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When it is impractical to rigorously assess all parts of complex systems, test engineers use defect detectors to focus their limited resources. In this article, we define some properties of an ideal defect detector and assess different methods of generating one. In the case study presented here, traditional methods of generating such detectors (e.g. reusing detectors from the literature, linear regression, model trees) were found to be inferior to those found via a PACE analysis.